diff --git a/src/manifest.json b/src/manifest.json
index 3c37234..fe0cccc 100644
--- a/src/manifest.json
+++ b/src/manifest.json
@@ -34,6 +34,26 @@
             "initialIsOpen": true,
             "path": "/uc/rag.md"
           }
+        },
+        {
+          "type": "internal-link",
+          "id": "ai_assistant",
+          "label": "Creating an AI Assistant",
+          "summary": "An assistant that temporarily retains conversations and understands by their meaning.",
+          "args": {
+            "initialIsOpen": true,
+            "path": "/uc/ai_assistant.md"
+          }
+        },
+        {
+          "type": "internal-link",
+          "id": "redis_use_cases_rag",
+          "label": "Building personalized recommendations",
+          "summary": "Not just keyword matching, but semantic understanding.",
+          "args": {
+            "initialIsOpen": true,
+            "path": "/uc/personalized_recommendations.md"
+          }
         }
       ]
     },
diff --git a/src/uc/ai_assistant.md b/src/uc/ai_assistant.md
new file mode 100644
index 0000000..2c4f7b5
--- /dev/null
+++ b/src/uc/ai_assistant.md
@@ -0,0 +1,204 @@
+This tutorial demonstrates how to build an AI assistant's memory system with Redis as its memory core.
+
+**Note**: Requires [Redis 8](https://hub.docker.com/_/redis/tags) for `HSETEX`, which adds per-field TTL for hashes, which is ideal for managing short-term memory with precision.
+
+### Architecture Overview
+| Layer | Description | Data type |
+| ---------- | ---------- | ---------- |
+| `Session History`| `Recent conversation context` | List |
+| `Rate Limiting` | `Per-user request throttling` | Hash |
+| `Knowledge Base` | `Long-term facts and preferences` | Hash |
+
+### Session History
+AI assistants need context from previous messages to provide coherent responses. Without conversation history, each interaction would be isolated and the AI couldn't reference what was discussed earlier. We can store conversation history using Redis lists - simple, ordered, and efficient for chat scenarios.
+
+```redis:[run_confirmation=true] Store conversation history
+// Add user message to session
+LPUSH user:alice:history:session_001 '{"type": "human", "content": "What is the weather like?", "timestamp": 1717935001}'
+
+// Add AI response
+LPUSH user:alice:history:session_001 '{"type": "ai", "content": "It is sunny with 75°F temperature.", "timestamp": 1717935002}'
+
+// Add another user message
+LPUSH user:alice:history:session_001 '{"type": "human", "content": "Should I bring an umbrella?", "timestamp": 1717935003}'
+
+// Add AI response
+LPUSH user:alice:history:session_001 '{"type": "ai", "content": "No umbrella needed today!", "timestamp": 1717935004}'
+```
+### Reading Conversation History
+Now we can retrieve conversation history to provide context to the AI.
+
+```redis:[run_confirmation=true] Read conversation history
+// Get last 5 messages (most recent first)
+LRANGE user:alice:history:session_001 0 4
+
+// Get all messages in session
+LRANGE user:alice:history:session_001 0 -1
+
+// Get specific message by index
+LINDEX user:alice:history:session_001 0
+
+// Check how many messages in session
+LLEN user:alice:history:session_001
+```
+### Session Expiration
+Without expiration, conversation history would accumulate indefinitely, consuming memory and potentially exposing sensitive information. TTL ensures privacy and efficient memory usage.
+
+```redis:[run_confirmation=true] Session expiration
+// Set session to expire in 24 hours
+EXPIRE user:alice:history:session_001 86400
+
+// Set session to expire in 1 hour
+EXPIRE user:alice:history:session_001 3600
+
+// Check remaining TTL
+TTL user:alice:history:session_001
+
+// Remove expiration (make persistent)
+PERSIST user:alice:history:session_001
+```
+
+### Rate Limiting
+Rate limiting prevents abuse and ensures fair resource usage. Without it, users could overwhelm the system with requests, degrading performance for everyone.
+
+```redis:[run_confirmation=true] Initialize Rate Limiting
+// First request - set counter with 1-minute TTL
+HSETEX user:alice:rate_limit EX 60 FIELDS 1 requests_per_minute 1
+
+// Check current request count
+HGET user:alice:rate_limit requests_per_minute
+```
+
+The `HINCR` command allows you to atomically increment the counter, preventing race conditions in high-concurrency scenarios.
+
+```redis:[run_confirmation=true] Increment Requests
+// Increment request counter
+HINCRBY user:alice:rate_limit requests_per_minute 1
+
+// Check if field exists and get count
+HEXISTS user:alice:rate_limit requests_per_minute
+HGET user:alice:rate_limit requests_per_minute
+
+// Check TTL on the hash
+TTL user:alice:rate_limit
+```
+
+Different time windows serve different purposes - per-minute prevents burst attacks, per-hour prevents sustained abuse, per-day enforces usage quotas.
+```redis:[run_confirmation=true] Rate Limiting with Different Time Windows
+// Set multiple rate limits with different TTLs
+HSETEX user:alice:rate_limit EX 60 FIELDS 2 requests_per_minute 1 requests_per_hour 1
+
+// Daily rate limit (24 hours)
+HSETEX user:alice:rate_limit EX 86400 FIELDS 1 requests_per_day 1
+
+// Check all rate limits
+HGETALL user:alice:rate_limit
+```
+
+### Knowledge Base (Persistent Memory)
+AI assistants become more helpful when they remember user preferences, important facts, and context across sessions. This creates a personalized experience that improves over time.
+Remembering user preferences (meeting times, communication style) enables the AI to provide more relevant and personalized responses without asking the same questions repeatedly.
+
+```redis:[run_confirmation=true] Store User Preferences
+// Always secure sensitive data using encryption at rest, access control (Redis ACLs), and comply with data protection laws (e.g., GDPR).
+// Store morning preference
+HSET user:alice:knowledge:pref:001 user_id "alice" content "prefers morning appointments before 10 AM" importance 9 timestamp 1717935000 embedding "\x40\x20\x00\x00\x3f\x80\x00\x00\x40\x40\x00\x00\x3f\x40\x00\x00\x40\x60\x00\x00\x40\x00\x00\x00\x3f\x00\x00\x00\x40\x80\x00\x00"
+
+// Storing communication preference
+HSET user:alice:knowledge:pref:002 user_id "alice" content "likes detailed explanations with examples" importance 8 timestamp 1717935000 embedding "\x3f\x40\x00\x00\x40\x60\x00\x00\x40\x00\x00\x00\x40\x20\x00\x00\x3f\x80\x00\x00\x40\x40\x00\x00\x40\x80\x00\x00\x3f\x00\x00\x00"
+
+// Store work schedule preference
+HSET user:alice:knowledge:pref:003 user_id "alice" content "works remotely on Fridays" importance 7 timestamp 1717935000 embedding "\x40\x80\x00\x00\x3f\x00\x00\x00\x40\x40\x00\x00\x40\x20\x00\x00\x3f\x80\x00\x00\x40\x60\x00\x00\x40\x00\x00\x00\x3f\x40\x00\x00"
+
+// Store health information
+HSET user:alice:knowledge:personal:001 user_id "alice" content "allergic to shellfish and nuts" importance 10 timestamp 1717935000 embedding "\x40\x00\x00\x00\x40\x20\x00\x00\x3f\x80\x00\x00\x40\x40\x00\x00\x40\x60\x00\x00\x3f\x40\x00\x00\x40\x80\x00\x00\x3f\x00\x00\x00"
+
+// Store pet information
+HSET user:alice:knowledge:personal:002 user_id "alice" content "has a golden retriever named Max, 3 years old" importance 7 timestamp 1717935000 embedding "\x3f\x80\x00\x00\x40\x40\x00\x00\x40\x00\x00\x00\x3f\x40\x00\x00\x40\x80\x00\x00\x40\x20\x00\x00\x3f\x00\x00\x00\x40\x60\x00\x00"
+
+// Store family information
+HSET user:alice:knowledge:personal:003 user_id "alice" content "married to Bob, two kids Sarah (8) and Tom (5)" importance 9 timestamp 1717935000 embedding "\x40\x60\x00\x00\x40\x00\x00\x00\x3f\x40\x00\x00\x40\x80\x00\x00\x40\x20\x00\x00\x3f\x80\x00\x00\x40\x40\x00\x00\x3f\x00\x00\x00"
+```
+
+### Vector Search: Semantic Memory Recall
+Traditional keyword search misses semantic meaning. When a user asks about "scheduling meetings," vector search can find relevant information about "prefers morning appointments" even though the keywords don't match exactly.
+
+Indexing persistent memory (knowledge base) for semantically meaningful search.
+
+```redis:[run_confirmation=true] Create a Vector Index
+// Create index for semantic search
+FT.CREATE idx:knowledge
+    ON HASH
+    PREFIX 1 user:
+    SCHEMA
+        user_id TAG
+        content TEXT
+        importance NUMERIC
+        timestamp NUMERIC
+        embedding VECTOR HNSW 6
+            TYPE FLOAT32
+            DIM 8 // DIM = embedding size, DIM 8 is just for demo purposes. In real use, embeddings are usually 128–1536 dimensions.
+            DISTANCE_METRIC COSINE
+```
+
+### Search for most relevant memory entries
+
+```redis:[run_confirmation=false] Find Top 5 Most Relevant Knowledge Items
+FT.SEARCH idx:knowledge 
+    "(@user_id:{alice}) => [KNN 5 @embedding $vec AS score]" 
+    PARAMS 2 vec "\x40\x00\x00\x00\x40\x20\x00\x00\x3f\x80\x00\x00\x40\x40\x00\x00\x40\x60\x00\x00\x3f\x40\x00\x00\x40\x80\x00\x00\x3f\x00\x00\x00"
+    RETURN 4 content importance score timestamp
+    SORTBY score ASC
+    DIALECT 2
+```
+
+```redis:[run_confirmation=false] Search High-Importance Items Only
+FT.SEARCH idx:knowledge
+    "(@user_id:{alice} @importance:[8 +inf]) => [KNN 3 @embedding $vec AS score]"
+    PARAMS 2 vec "\x40\x40\x00\x00\x3f\x00\x00\x00\x40\x80\x00\x00\x40\x20\x00\x00\x3f\x80\x00\x00\x40\x60\x00\x00\x40\x00\x00\x00\x3f\x40\x00\x00"
+    RETURN 4 content importance score timestamp
+    SORTBY score ASC
+    DIALECT 2
+```
+```redis:[run_confirmation=false] Search Recent Knowledge Only
+FT.SEARCH idx:knowledge
+    "(@user_id:{alice} @timestamp:[1717935000 +inf]) => [KNN 5 @embedding $vec AS score]"
+    PARAMS 2 vec "\x3f\x80\x00\x00\x40\x40\x00\x00\x40\x00\x00\x00\x3f\x40\x00\x00\x40\x80\x00\x00\x40\x20\x00\x00\x3f\x00\x00\x00\x40\x60\x00\x00"
+    RETURN 3 content timestamp score
+    SORTBY score ASC
+    DIALECT 2
+```
+
+### Memory State Monitoring
+Understanding what's stored in memory helps debug issues, optimize performance, and ensure data quality. It's also essential for user privacy compliance.
+```redis:[run_confirmation=false] Monitor user sessions
+// Scan 10,000 keys to find user sessions
+SCAN 0 MATCH user:*:history:* COUNT 10000
+
+// Get session statistics
+LLEN user:alice:history:session_001
+TTL user:alice:history:session_001
+```
+### Data Cleanup
+```redis:[run_confirmation=true] Delete user data
+// Remove all user data (GDPR compliance)
+DEL user:alice:history:session_001
+DEL user:alice:history:session_002
+DEL user:alice:rate_limit
+DEL user:alice:knowledge:pref:001
+DEL user:alice:knowledge:pref:002
+DEL user:alice:knowledge:pref:003
+DEL user:alice:knowledge:personal:001
+DEL user:alice:knowledge:personal:002
+DEL user:alice:knowledge:personal:003
+DEL user:alice:knowledge:work:001
+DEL user:alice:knowledge:work:002
+DEL user:alice:knowledge:work:003
+```
+
+### Next Steps
+Now that your assistant has memory and meaning, you can:
+    - Combine with RAG Pipelines
+    - Use sentence-transformers to generate high-dimensional vectors
+    - Add [Redis Flex](https://redis.io/solutions/flex/?utm_source=redisinsight&utm_medium=app&utm_campaign=tutorials) for fallback persistence
+    - Use Redis ACLs to isolate users, enforce quotas, and monitor usage
diff --git a/src/uc/personalized_recommendations.md b/src/uc/personalized_recommendations.md
new file mode 100644
index 0000000..fccbae4
--- /dev/null
+++ b/src/uc/personalized_recommendations.md
@@ -0,0 +1,164 @@
+Imagine building a movie recommendation app that goes beyond keyword matching. Your users get intuitive, meaningful suggestions based on the true meaning of movie plots — powered by semantic understanding.
+
+### Store Movie Documents with Vector Embeddings
+Semantic search uses vector embeddings — numeric representations of text that capture meaning, enabling search by intent rather than keywords.
+
+We'll import a dataset of plot summaries, each paired with an embedding vector.
+
+Each movie is stored as a JSON document with:
+  - `title`, `genres`, `year`, `plot`
+  - `embedding`: a binary-encoded `FLOAT32[]` for vector similarity, generated via sentence-transformer models or similar.
+
+```redis:[run_confirmation=true] Upload Movies
+// Demo uses 8 DIM embeddings; production typically uses 128–1536D.
+JSON.SET movie:001 $ '{"title":"Toy Story","genres":["Animation","Comedy","Family"],"plot":"Toys come to life when humans arent around.","year":1995,"embedding":[0.22,0.04,0.33,0.12,-0.02,0.17,0.09,0.01]}'
+JSON.SET movie:002 $ '{"title":"Inside Out","genres":["Animation","Comedy","Drama"],"plot":"Emotions guide a young girl through change.","year":2015,"embedding":[0.20,0.03,0.31,0.11,-0.03,0.16,0.08,0.02]}'
+JSON.SET movie:003 $ '{"title":"Whiplash","genres":["Drama","Music"],"plot":"A young drummer is pushed to greatness.","year":2014,"embedding":[0.14,0.01,0.22,0.08,-0.07,0.10,0.04,0.00]}'
+JSON.SET movie:004 $ '{"title":"La La Land","genres":["Drama","Music","Romance"],"plot":"A jazz musician falls in love in LA.","year":2016,"embedding":[0.15,0.03,0.23,0.09,-0.08,0.14,0.06,0.01]}'
+JSON.SET movie:005 $ '{"title":"The Matrix","genres":["Action","Sci-Fi"],"plot":"A hacker discovers reality is a simulation.","year":1999,"embedding":[0.12,-0.03,0.25,0.04,-0.10,0.09,0.05,-0.02]}'
+JSON.SET movie:006 $ '{"title":"Inception","genres":["Action","Adventure","Sci-Fi"],"plot":"A thief steals information through dreams.","year":2010,"embedding":[0.14,-0.01,0.27,0.06,-0.09,0.10,0.04,-0.03]}'
+JSON.SET movie:007 $ '{"title":"Tenet","genres":["Action","Sci-Fi","Thriller"],"plot":"Time-inversion to prevent World War III.","year":2020,"embedding":[0.13,-0.06,0.29,0.05,-0.11,0.12,0.06,-0.01]}'
+JSON.SET movie:008 $ '{"title":"Finding Nemo","genres":["Animation","Adventure","Family"],"plot":"A clownfish searches for his son.","year":2003,"embedding":[0.18,0.02,0.30,0.10,-0.05,0.15,0.07,0.01]}'
+JSON.SET movie:009 $ '{"title":"Coco","genres":["Animation","Family","Music"],"plot":"A boy enters the Land of the Dead.","year":2017,"embedding":[0.21,0.04,0.34,0.13,-0.02,0.19,0.10,0.02]}'
+JSON.SET movie:010 $ '{"title":"Soul","genres":["Animation","Adventure","Comedy"],"plot":"A jazz musician explores the afterlife.","year":2020,"embedding":[0.16,0.02,0.28,0.10,-0.06,0.13,0.07,0.00]}'
+JSON.SET movie:011 $ '{"title":"The Dark Knight","genres":["Action","Crime","Drama"],"plot":"Batman fights the Joker.","year":2008,"embedding":[0.12,-0.03,0.25,0.04,-0.09,0.10,0.05,-0.02]}'
+JSON.SET movie:012 $ '{"title":"Frozen","genres":["Animation","Adventure","Comedy"],"plot":"A princess sets off to find her sister.","year":2013,"embedding":[0.22,0.04,0.33,0.12,-0.03,0.18,0.10,0.02]}'
+JSON.SET movie:013 $ '{"title":"The Lion King","genres":["Animation","Adventure","Drama"],"plot":"A lion prince flees and returns.","year":1994,"embedding":[0.19,0.02,0.32,0.10,-0.04,0.18,0.09,0.03]}'
+JSON.SET movie:014 $ '{"title":"Shrek","genres":["Animation","Adventure","Comedy"],"plot":"An ogre rescues a princess.","year":2001,"embedding":[0.21,0.03,0.32,0.11,-0.04,0.17,0.09,0.01]}'
+JSON.SET movie:015 $ '{"title":"The Social Network","genres":["Biography","Drama"],"plot":"The rise of Facebook and its creator.","year":2010,"embedding":[0.10,-0.01,0.21,0.05,-0.07,0.06,0.03,-0.02]}'
+JSON.SET movie:016 $ '{"title":"Guardians of the Galaxy","genres":["Action","Adventure","Sci-Fi"],"plot":"A group of intergalactic criminals must save the universe.","year":2014,"embedding":[0.13,0.00,0.28,0.07,-0.08,0.11,0.05,-0.01]}'
+JSON.SET movie:017 $ '{"title":"Moana","genres":["Animation","Adventure","Family"],"plot":"A young girl sets sail to save her island.","year":2016,"embedding":[0.20,0.03,0.33,0.12,-0.03,0.17,0.09,0.02]}'
+JSON.SET movie:018 $ '{"title":"Whale Rider","genres":["Drama","Family"],"plot":"A girl fights tradition to become chief.","year":2002,"embedding":[0.15,0.01,0.25,0.09,-0.05,0.12,0.06,0.00]}'
+JSON.SET movie:019 $ '{"title":"Rocketman","genres":["Biography","Drama","Music"],"plot":"The story of Elton Johns breakthrough years.","year":2019,"embedding":[0.14,0.01,0.22,0.07,-0.06,0.11,0.05,0.01]}'
+JSON.SET movie:020 $ '{"title":"Amadeus","genres":["Biography","Drama","Music"],"plot":"The rivalry between Mozart and Salieri.","year":1984,"embedding":[0.13,0.00,0.20,0.06,-0.07,0.10,0.04,0.00]}'
+JSON.SET movie:021 $ '{"title":"The Sound of Music","genres":["Biography","Drama","Music"],"plot":"A governess brings music to a family.","year":1965,"embedding":[0.14,0.02,0.21,0.07,-0.06,0.11,0.05,0.01]}'
+JSON.SET movie:022 $ '{"title":"Les Miserables","genres":["Drama","Music","Romance"],"plot":"The struggles of ex-convict Jean Valjean.","year":2012,"embedding":[0.13,0.01,0.23,0.08,-0.07,0.12,0.05,0.01]}'
+JSON.SET movie:023 $ '{"title":"The Greatest Showman","genres":["Biography","Drama","Music"],"plot":"The story of P.T. Barnum and his circus.","year":2017,"embedding":[0.15,0.03,0.25,0.09,-0.05,0.13,0.06,0.02]}'
+JSON.SET movie:024 $ '{"title":"A Star Is Born","genres":["Drama","Music","Romance"],"plot":"A musician helps a young singer find fame.","year":2018,"embedding":[0.14,0.02,0.24,0.08,-0.06,0.12,0.05,0.01]}'
+JSON.SET movie:025 $ '{"title":"Mad Max: Fury Road","genres":["Action","Adventure","Sci-Fi"],"plot":"In a post-apocalyptic wasteland, Max helps rebels escape.","year":2015,"embedding":[0.11,-0.02,0.26,0.05,-0.10,0.08,0.05,-0.02]}'
+JSON.SET movie:026 $ '{"title":"Blade Runner 2049","genres":["Sci-Fi","Thriller"],"plot":"A new blade runner uncovers secrets.","year":2017,"embedding":[0.12,-0.03,0.27,0.06,-0.09,0.09,0.06,-0.01]}'
+JSON.SET movie:027 $ '{"title":"Arrival","genres":["Drama","Sci-Fi","Thriller"],"plot":"A linguist communicates with aliens.","year":2016,"embedding":[0.13,-0.01,0.28,0.07,-0.08,0.11,0.05,-0.01]}'
+JSON.SET movie:028 $ '{"title":"Interstellar","genres":["Adventure","Drama","Sci-Fi"],"plot":"Explorers travel through a wormhole in space.","year":2014,"embedding":[0.14,-0.02,0.29,0.08,-0.09,0.12,0.06,-0.02]}'
+JSON.SET movie:029 $ '{"title":"E.T. the Extra-Terrestrial","genres":["Family","Sci-Fi"],"plot":"A boy befriends an alien.","year":1982,"embedding":[0.17,0.01,0.31,0.10,-0.06,0.15,0.07,0.01]}'
+JSON.SET movie:030 $ '{"title":"The Avengers","genres":["Action","Adventure","Sci-Fi"],"plot":"Superheroes team up to save the world.","year":2012,"embedding":[0.13,0.00,0.27,0.07,-0.08,0.11,0.06,-0.01]}'
+JSON.SET movie:031 $ '{"title":"Guardians of the Galaxy Vol. 2","genres":["Action","Adventure","Comedy"],"plot":"The Guardians fight to protect the galaxy.","year":2017,"embedding":[0.15,0.01,0.28,0.09,-0.07,0.13,0.07,0.01]}'
+JSON.SET movie:032 $ '{"title":"Up","genres":["Animation","Adventure","Comedy"],"plot":"An old man goes on an adventure in his flying house.","year":2009,"embedding":[0.21,0.04,0.32,0.11,-0.04,0.16,0.09,0.02]}'
+JSON.SET movie:033 $ '{"title":"Zootopia","genres":["Animation","Adventure","Comedy"],"plot":"A bunny cop solves a mystery in a city of animals.","year":2016,"embedding":[0.20,0.03,0.31,0.10,-0.05,0.15,0.08,0.01]}'
+JSON.SET movie:034 $ '{"title":"Big Hero 6","genres":["Animation","Action","Comedy"],"plot":"A robotics prodigy teams with friends to fight crime.","year":2014,"embedding":[0.19,0.02,0.30,0.09,-0.05,0.14,0.08,0.01]}'
+JSON.SET movie:035 $ '{"title":"The Prestige","genres":["Drama","Mystery","Sci-Fi"],"plot":"Two magicians engage in a deadly rivalry.","year":2006,"embedding":[0.12,-0.02,0.24,0.06,-0.08,0.10,0.05,-0.01]}'
+JSON.SET movie:036 $ '{"title":"Dunkirk","genres":["Action","Drama","History"],"plot":"Allied soldiers are evacuated during WWII.","year":2017,"embedding":[0.10,-0.03,0.22,0.05,-0.09,0.07,0.04,-0.02]}'
+JSON.SET movie:037 $ '{"title":"Jumanji: Welcome to the Jungle","genres":["Action","Adventure","Comedy"],"plot":"Teens trapped in a video game jungle.","year":2017,"embedding":[0.16,0.01,0.27,0.08,-0.06,0.12,0.06,0.01]}'
+JSON.SET movie:038 $ '{"title":"Cinderella","genres":["Animation","Family","Fantasy"],"plot":"A young girl overcomes her cruel stepmother.","year":1950,"embedding":[0.19,0.03,0.31,0.11,-0.04,0.16,0.08,0.02]}'
+JSON.SET movie:039 $ '{"title":"Mulan","genres":["Animation","Adventure","Drama"],"plot":"A young woman disguises as a soldier.","year":1998,"embedding":[0.20,0.03,0.32,0.11,-0.04,0.17,0.09,0.02]}'
+JSON.SET movie:040 $ '{"title":"Beauty and the Beast","genres":["Animation","Family","Fantasy"],"plot":"A young woman falls in love with a beast.","year":1991,"embedding":[0.18,0.02,0.30,0.10,-0.05,0.15,0.08,0.01]}'
+JSON.SET movie:041 $ '{"title":"The Godfather","genres":["Crime","Drama"],"plot":"The aging patriarch of an organized crime dynasty transfers control to his son.","year":1972,"embedding":[0.11,-0.04,0.24,0.06,-0.10,0.07,0.05,-0.03]}'
+JSON.SET movie:042 $ '{"title":"Pulp Fiction","genres":["Crime","Drama"],"plot":"The lives of two mob hitmen, a boxer, and others intertwine.","year":1994,"embedding":[0.12,-0.03,0.23,0.07,-0.09,0.09,0.04,-0.01]}'
+JSON.SET movie:043 $ '{"title":"Forrest Gump","genres":["Drama","Romance"],"plot":"The presidencies of Kennedy and Johnson through the eyes of Forrest.","year":1994,"embedding":[0.14,0.01,0.26,0.08,-0.07,0.11,0.06,0.01]}'
+JSON.SET movie:044 $ '{"title":"Gladiator","genres":["Action","Drama"],"plot":"A former Roman General seeks revenge.","year":2000,"embedding":[0.13,0.00,0.25,0.07,-0.08,0.10,0.05,0.00]}'
+JSON.SET movie:045 $ '{"title":"Titanic","genres":["Drama","Romance"],"plot":"A seventeen-year-old aristocrat falls in love with a kind but poor artist.","year":1997,"embedding":[0.15,0.02,0.28,0.09,-0.06,0.13,0.06,0.01]}'
+JSON.SET movie:046 $ '{"title":"Jurassic Park","genres":["Adventure","Sci-Fi"],"plot":"Scientists clone dinosaurs for a theme park.","year":1993,"embedding":[0.14,-0.01,0.26,0.08,-0.07,0.11,0.06,0.00]}'
+JSON.SET movie:047 $ '{"title":"The Shawshank Redemption","genres":["Drama"],"plot":"Two imprisoned men bond over a number of years.","year":1994,"embedding":[0.15,0.00,0.27,0.09,-0.06,0.12,0.07,0.01]}'
+JSON.SET movie:048 $ '{"title":"Fight Club","genres":["Drama"],"plot":"An insomniac and a soap maker form an underground fight club.","year":1999,"embedding":[0.13,-0.02,0.24,0.07,-0.08,0.10,0.05,-0.01]}'
+JSON.SET movie:049 $ '{"title":"The Silence of the Lambs","genres":["Crime","Drama","Thriller"],"plot":"A young FBI cadet seeks help from an imprisoned cannibal.","year":1991,"embedding":[0.11,-0.03,0.22,0.06,-0.09,0.08,0.04,-0.02]}'
+JSON.SET movie:050 $ '{"title":"The Departed","genres":["Crime","Drama","Thriller"],"plot":"An undercover cop and a mole in the police attempt to identify each other.","year":2006,"embedding":[0.12,-0.02,0.23,0.07,-0.08,0.09,0.05,-0.01]}'
+JSON.SET movie:51 $ '{"title":"Saturday Night Fever","year":1977,"genres":["Drama","Music"],"plot":"A young man finds escape from his mundane life through disco dancing.","embedding":[0.154,-0.050,0.300,0.150,-0.150,0.154,0.034,-0.118]}'
+JSON.SET movie:52 $ '{"title":"The Rose","year":1979,"genres":["Music","Drama"],"plot":"A rock star struggles with fame, addiction, and love.","embedding":[0.144,-0.055,0.295,0.145,-0.160,0.150,0.030,-0.120]}'
+JSON.SET movie:53 $ '{"title":"Cabaret","year":1972,"genres":["Drama","Music"],"plot":"A performer and a writer navigate love and politics in pre-WWII Berlin.","embedding":[0.151,-0.052,0.297,0.148,-0.152,0.150,0.031,-0.121]}'
+JSON.SET movie:54 $ '{"title":"Tommy","year":1975,"genres":["Drama","Music","Fantasy"],"plot":"A deaf and blind boy becomes a pinball champion and religious figure.","embedding":[0.149,-0.051,0.301,0.149,-0.151,0.153,0.033,-0.119]}'
+JSON.SET movie:55 $ '{"title":"All That Jazz","year":1979,"genres":["Drama","Music"],"plot":"A choreographer reflects on his life and art while facing death.","embedding":[0.153,-0.049,0.299,0.151,-0.149,0.152,0.032,-0.117]}'
+```
+
+### Create a Vector-Enabled Search Index
+Redis stores movie data as JSON documents with text fields (title, genres, plot) and vector embeddings. Indexing enables fast filtering and approximate nearest neighbor (ANN) searches on embeddings.
+
+```redis:[run_confirmation=true] Create a Vector Index
+FT.CREATE idx:movies ON JSON PREFIX 1 "movie:" SCHEMA
+  $.title AS title TEXT
+  $.genres[*] AS genres TAG
+  $.plot AS plot TEXT
+  $.year AS year NUMERIC
+  $.embedding AS embedding VECTOR FLAT
+    6
+        TYPE FLOAT32
+        DIM 8 // DIM = embedding size, DIM 8 is just for demo purposes. In real use, embeddings are usually 128–1536 dimensions.
+        DISTANCE_METRIC COSINE // COSINE = measures semantic closeness
+ ```
+
+This sets the stage for combined textual and semantic search.
+
+### Semantic Search by Query Embedding
+When users search “I want a fun animated movie about toys and friendship”, sentence-transformers models can convert the text into a vector. You can store and query this vector in Redis for semantic search.
+
+```redis:[run_confirmation=false] Search Per Plot
+FT.SEARCH idx:movies "*=>[KNN 3 @embedding $vec AS score]"
+  PARAMS 2 vec "\x9a\x99\x19\x3f\xcd\xcc\xcc\x3d\x9a\x99\x4c\x3f\x9a\x99\x33\x3e\x9a\x99\x33\x3f\xcd\xcc\x66\x3e\xcd\xcc\xcc\x3d\xcd\xcc\x4c\x3e"
+  SORTBY score
+  RETURN 3 title plot score
+  DIALECT 2
+```
+
+Redis returns top movies with embeddings close to the query vector - Toy Story ranks first, even if keywords don’t exactly match.
+
+### Adding Filters for Hybrid Search
+
+Now, let’s find music movies:
+“A feel-good film about music and students.”
+
+You can combine a genre filter with vector similarity:
+
+```redis:[run_confirmation=false] Search Per Genre
+FT.SEARCH idx:movies "@genres:{Music} =>[KNN 5 @embedding $vec AS score]"
+  PARAMS 2 vec "\x9a\x99\x1d\x3e\xcd\xcc\x4c\xbd\x9a\x99\x99\x3e\x9a\x99\x19\x3e\x9a\x99\x19\xbe\x9a\x99\x1d\x3e\xcd\xcc\x0c\x3e\x9a\x99\xf1\xbc"
+  SORTBY score
+  RETURN 3 title genres score
+  DIALECT 2
+```
+
+This hybrid query uses Redis’s tagging system plus vector search, improving relevance.
+
+### Using Embeddings of Existing Movies for Recommendations
+
+Let’s say you love Inception and want similar movies. Let’s retrieve the Inception plot embedding and use it as the query vector:
+```redis:[run_confirmation=false] Get the Embedding From the Movie Document
+JSON.GET movie:006 $.embedding
+```
+Now let’s run vector similarity search using that embedding as a binary blob (`FLOAT32`-packed):
+
+```redis:[run_confirmation=false] Search for Similar Movies
+FT.SEARCH idx:movies "*=>[KNN 5 @embedding $vec AS score]" 
+    PARAMS 2 vec "\xCD\xCC\x56\x3E\x9A\x99\xF3\xBC\xCD\xCC\x00\x3F\x66\x66\x34\x3E\xC6\xF5\x1B\xBE\x9A\x99\x4D\x3E\x9A\x99\x99\x3D\x9A\x99\xB5\xBD" 
+    SORTBY score 
+    RETURN 2 title score 
+    DIALECT 2
+```
+
+Redis finds movies like Arrival and The Departed, showcasing content-based recommendation with semantic similarity.
+
+### Combining Metadata Filters with Vector Search
+Let’s find classic musical rebellion films from the 90s:
+```redis:[run_confirmation=false] Search for Classical Musical Rebellion Films
+FT.SEARCH idx:movies "(@genres:{Music} @year:[1970 1979]) =>[KNN 5 @embedding $vec AS score]"
+  PARAMS 2 vec "\x9a\x99\x1d\x3e\xcd\xcc\x4c\xbd\x9a\x99\x99\x3e\x9a\x99\x19\x3e\x9a\x99\x19\xbe\x9a\x99\x1d\x3e\xcd\xcc\x0c\x3e\x9a\x99\xf1\xbc"
+  SORTBY score
+  RETURN 4 title year genres score
+  DIALECT 2
+```
+This shows how Redis vector search works seamlessly with numeric and tag filters.
+
+### Personalizing Recommendations
+Say we like Animated and Sci-Fi movies. You can personalize results by filtering the vector search:
+```redis:[run_confirmation=false] Search Per Genres
+FT.SEARCH idx:movies '@genres:{"Animated"|"Sci-Fi"} =>[KNN 5 @embedding $vec AS score]'
+   PARAMS 2 vec "\x9a\x99\x1d\x3e\xcd\xcc\x4c\xbd\x9a\x99\x99\x3e\x9a\x99\x19\x3e\x9a\x99\x19\xbe\x9a\x99\x1d\x3e\xcd\xcc\x0c\x3e\x9a\x99\xf1\xbc"
+   SORTBY score
+   RETURN 3 title genres score
+   DIALECT 2
+```
+
+This makes Redis recommendations responsive to evolving user preferences without retraining embeddings.
+
+### Next Steps
+  - Learn more about building personalized recommendations with this [workshop](https://github.com/redis-developer/redis-movies-searcher/tree/springio-2025-workshop).
+  - Build a UI for natural language queries that delivers instant semantic recommendations.
+  - Add personalization by merging user preferences with semantic search.
+  - Explore advanced vector search methods like HNSW indexing for large datasets.
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